AI Development Lifecycle (AI-DLC) Engineer

Job Description

Job Description

AI Development Lifecycle (AI-DLC) Engineer / Consultant
NOTE: We cannot consider Third Party candidates for this position. Please do not contact or email regarding this position.

Location: Minneapolis, MN / Candidates considered throughout the U.S.

Work Arrangement: Candidates must be open to remote, hybrid, or onsite work based on project requirements.

We are seeking experienced AI Development Lifecycle (AI-DLC) professionals to help enterprise technology teams adopt and scale AI-assisted software development practices. This role will work across engineering, architecture, product, QA, data, and technology leadership teams to integrate AI into the way software solutions are planned, designed, developed, tested, deployed, and maintained.

The ideal candidate combines a strong software engineering foundation with hands-on experience using generative AI and AI-assisted development tools. This individual should be capable of both implementing AI-DLC practices and helping engineering teams adopt new tools, workflows, standards, and development approaches.

Responsibilities:

• Help define, implement, and continuously improve AI Development Lifecycle practices across enterprise technology teams.

• Work hands-on with software engineering, architecture, product, QA, UX, data, and DevOps teams to integrate AI into existing development processes.

• Identify opportunities to use AI to accelerate requirements analysis, solution design, coding, modernization, testing, documentation, troubleshooting, and production support.

• Implement and promote AI-assisted development techniques, including specification-driven development, prompt engineering, context engineering, code generation, refactoring, test generation, and documentation.

• Establish reusable prompts, development patterns, reference implementations, templates, coding standards, and knowledge assets.

• Evaluate and implement AI development tools and platforms, including coding assistants, AI agents, LLM platforms, orchestration frameworks, and related technologies.

• Develop approaches for validating AI-generated code and technical outputs for accuracy, quality, security, maintainability, and compliance.

• Establish appropriate human-in-the-loop controls, evaluation frameworks, testing practices, and governance processes.

• Support modernization of existing applications by using AI to understand legacy codebases, dependencies, architecture, and business logic.

• Integrate AI-assisted development practices with source control, CI/CD, automated testing, DevOps, MLOps/LLMOps, and enterprise engineering toolchains.

• Coach engineering teams on effective and responsible use of AI-assisted development technologies.

• Capture lessons learned and convert successful approaches into reusable enterprise practices and standards.

• Measure and communicate the impact of AI-DLC adoption, including improvements in development velocity, quality, productivity, and time to delivery.

Required Qualifications:

• 5+ years of experience in software engineering, application development, DevOps, solution architecture, technical consulting, or related disciplines.

• Strong understanding of the traditional Software Development Lifecycle (SDLC) and modern Agile/DevOps engineering practices.

• Hands-on experience with generative AI, LLMs, AI coding assistants, or AI-enabled software development tools.

• Experience with modern programming languages such as Python, Java, JavaScript/TypeScript, C#, or similar technologies.

• Understanding of APIs, application architecture, cloud platforms, data integration, source control, automated testing, and CI/CD.

• Experience using or evaluating AI-assisted development tools such as GitHub Copilot, Claude Code, Cursor, or similar platforms.

• Ability to assess AI-generated output and identify issues involving accuracy, security, maintainability, hallucination, or unnecessary complexity.

• Strong communication and facilitation skills with the ability to work across engineering, architecture, product, QA, and business teams.

• Ability to operate effectively in an evolving environment where AI development standards and technologies are rapidly changing.

Preferred Qualifications:

• Experience implementing AI-DLC, AI-native engineering, or AI-assisted SDLC practices within a large enterprise.

• Experience with prompt engineering, context engineering, specification-driven development, AI agents, RAG, or related generative AI techniques.

• Familiarity with AI evaluation frameworks, observability, MLOps/LLMOps, and responsible AI practices.

• Experience establishing engineering standards, reusable development patterns, reference architectures, or developer enablement programs.

• Experience coaching development teams or leading adoption of new software engineering methodologies or technologies.

• Experience with Azure, AWS, or GCP AI services and enterprise development platforms.

• Healthcare industry experience, particularly within a healthcare payer environment, is highly desirable.